Analysis and structuring diagnostic large volume data of technical condition of complex equipment in transport

Abstract

Abstract The paper presents the results of the classification analysis model for structuring of processed large volumes of heterogeneous diagnostic data about the technical state of complex equipment in transport development and research. Concept for the description and structuring of big data is proposed based on the formation of a metadata scheme using logical breakdown of all technical diagnostic data on the output variable - the technical condition of complex technical equipment in transport. A functional assessment of the technical condition complex technical system’s elements in transport is developed based on the application of methods for assessing structural and functional risks of failures. The article presents the results of assessing the accuracy of the input data sets classification using created decision trees models to effectively structuring and presenting the data in order to ensure that the procedures for their further analysis are performed. As a result of using the developed simulation model of structuring and presenting large heterogeneous diagnostic data volumes about the state of complex technical equipment in transport the time costs were reduced and the efficiency of analytical operations to study data for solving diagnostic problems and predicting complex system’s technical condition was improved.